Overview

Dataset statistics

Number of variables35
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory883.1 B

Variable types

Numeric13
Categorical20
Text1
Boolean1

Alerts

nombre_heures_travailless has constant value "80"Constant
nombre_employee_sous_responsabilite has constant value "1"Constant
ayant_enfants has constant value "True"Constant
a_quitte_l_entreprise is highly overall correlated with idHigh correlation
age is highly overall correlated with annee_experience_totaleHigh correlation
annee_experience_totale is highly overall correlated with age and 3 other fieldsHigh correlation
annees_dans_l_entreprise is highly overall correlated with annee_experience_totale and 3 other fieldsHigh correlation
annees_dans_le_poste_actuel is highly overall correlated with annees_dans_l_entreprise and 2 other fieldsHigh correlation
annees_depuis_la_derniere_promotion is highly overall correlated with annees_dans_l_entreprise and 1 other fieldsHigh correlation
annes_sous_responsable_actuel is highly overall correlated with annees_dans_l_entreprise and 1 other fieldsHigh correlation
augementation_salaire_precedente is highly overall correlated with id and 1 other fieldsHigh correlation
code_sondage is highly overall correlated with id and 1 other fieldsHigh correlation
departement is highly overall correlated with domaine_etude and 2 other fieldsHigh correlation
domaine_etude is highly overall correlated with departement and 1 other fieldsHigh correlation
frequence_deplacement is highly overall correlated with idHigh correlation
genre is highly overall correlated with idHigh correlation
heure_supplementaires is highly overall correlated with idHigh correlation
id is highly overall correlated with a_quitte_l_entreprise and 19 other fieldsHigh correlation
id_employee is highly overall correlated with code_sondage and 1 other fieldsHigh correlation
niveau_education is highly overall correlated with idHigh correlation
niveau_hierarchique_poste is highly overall correlated with annee_experience_totale and 3 other fieldsHigh correlation
nombre_participation_pee is highly overall correlated with id and 1 other fieldsHigh correlation
note_evaluation_actuelle is highly overall correlated with augementation_salaire_precedente and 1 other fieldsHigh correlation
note_evaluation_precedente is highly overall correlated with idHigh correlation
poste is highly overall correlated with departement and 2 other fieldsHigh correlation
revenu_mensuel is highly overall correlated with annee_experience_totale and 1 other fieldsHigh correlation
satisfaction_employee_environnement is highly overall correlated with idHigh correlation
satisfaction_employee_equilibre_pro_perso is highly overall correlated with idHigh correlation
satisfaction_employee_equipe is highly overall correlated with idHigh correlation
satisfaction_employee_nature_travail is highly overall correlated with idHigh correlation
statut_marital is highly overall correlated with id and 1 other fieldsHigh correlation
id_employee has unique valuesUnique
id has unique valuesUnique
eval_number has unique valuesUnique
code_sondage has unique valuesUnique
nombre_experiences_precedentes has 197 (13.4%) zerosZeros
annees_dans_l_entreprise has 44 (3.0%) zerosZeros
annees_dans_le_poste_actuel has 244 (16.6%) zerosZeros
nb_formations_suivies has 54 (3.7%) zerosZeros
annees_depuis_la_derniere_promotion has 581 (39.5%) zerosZeros
annes_sous_responsable_actuel has 263 (17.9%) zerosZeros

Reproduction

Analysis started2026-01-16 15:59:41.197137
Analysis finished2026-01-16 16:03:13.977603
Duration3 minutes and 32.78 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

id_employee
Real number (ℝ)

High correlation  Unique 

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.8653
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:14.194207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.02433
Coefficient of variation (CV)0.58741801
Kurtosis-1.2231789
Mean1024.8653
Median Absolute Deviation (MAD)533.5
Skewness0.01657402
Sum1506552
Variance362433.3
MonotonicityStrictly increasing
2026-01-16T17:03:14.384538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20681
 
0.1%
11
 
0.1%
21
 
0.1%
41
 
0.1%
51
 
0.1%
71
 
0.1%
81
 
0.1%
101
 
0.1%
111
 
0.1%
121
 
0.1%
Other values (1460)1460
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
41
0.1%
51
0.1%
71
0.1%
81
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
ValueCountFrequency (%)
20681
0.1%
20651
0.1%
20641
0.1%
20621
0.1%
20611
0.1%
20601
0.1%
20571
0.1%
20561
0.1%
20551
0.1%
20541
0.1%

age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:14.510651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2026-01-16T17:03:14.636368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3578
 
5.3%
3477
 
5.2%
3669
 
4.7%
3169
 
4.7%
2968
 
4.6%
3261
 
4.1%
3060
 
4.1%
3858
 
3.9%
3358
 
3.9%
4057
 
3.9%
Other values (33)815
55.4%
ValueCountFrequency (%)
188
 
0.5%
199
 
0.6%
2011
 
0.7%
2113
 
0.9%
2216
 
1.1%
2314
 
1.0%
2426
1.8%
2526
1.8%
2639
2.7%
2748
3.3%
ValueCountFrequency (%)
605
 
0.3%
5910
0.7%
5814
1.0%
574
 
0.3%
5614
1.0%
5522
1.5%
5418
1.2%
5319
1.3%
5218
1.2%
5119
1.3%

genre
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
M
882 
F
588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M882
60.0%
F588
40.0%

Length

2026-01-16T17:03:14.778132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:14.892596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
m882
60.0%
f588
40.0%

Most occurring characters

ValueCountFrequency (%)
M882
60.0%
F588
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M882
60.0%
F588
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M882
60.0%
F588
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M882
60.0%
F588
40.0%

revenu_mensuel
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:14.999065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2026-01-16T17:03:15.140692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
26103
 
0.2%
25593
 
0.2%
24513
 
0.2%
61423
 
0.2%
55623
 
0.2%
24043
 
0.2%
23803
 
0.2%
63473
 
0.2%
34523
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

statut_marital
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size123.3 KiB
Marié(e)
673 
Célibataire
470 
Divorcé(e)
327 

Length

Max length11
Median length10
Mean length9.4040816
Min length8

Characters and Unicode

Total characters13824
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCélibataire
2nd rowMarié(e)
3rd rowCélibataire
4th rowMarié(e)
5th rowMarié(e)

Common Values

ValueCountFrequency (%)
Marié(e)673
45.8%
Célibataire470
32.0%
Divorcé(e)327
22.2%

Length

2026-01-16T17:03:15.505253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:15.615188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
marié(e673
45.8%
célibataire470
32.0%
divorcé(e327
22.2%

Most occurring characters

ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)13824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

departement
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size85.4 KiB
Consulting
961 
Commercial
446 
Ressources Humaines
 
63

Length

Max length19
Median length10
Mean length10.385714
Min length10

Characters and Unicode

Total characters15267
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCommercial
2nd rowConsulting
3rd rowConsulting
4th rowConsulting
5th rowConsulting

Common Values

ValueCountFrequency (%)
Consulting961
65.4%
Commercial446
30.3%
Ressources Humaines63
 
4.3%

Length

2026-01-16T17:03:15.725717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:15.835818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
consulting961
62.7%
commercial446
29.1%
ressources63
 
4.1%
humaines63
 
4.1%

Most occurring characters

ValueCountFrequency (%)
n1985
13.0%
i1470
9.6%
o1470
9.6%
C1407
9.2%
l1407
9.2%
s1213
7.9%
u1087
7.1%
t961
6.3%
g961
6.3%
m955
6.3%
Other values (7)2351
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)15267
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n1985
13.0%
i1470
9.6%
o1470
9.6%
C1407
9.2%
l1407
9.2%
s1213
7.9%
u1087
7.1%
t961
6.3%
g961
6.3%
m955
6.3%
Other values (7)2351
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15267
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n1985
13.0%
i1470
9.6%
o1470
9.6%
C1407
9.2%
l1407
9.2%
s1213
7.9%
u1087
7.1%
t961
6.3%
g961
6.3%
m955
6.3%
Other values (7)2351
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15267
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n1985
13.0%
i1470
9.6%
o1470
9.6%
C1407
9.2%
l1407
9.2%
s1213
7.9%
u1087
7.1%
t961
6.3%
g961
6.3%
m955
6.3%
Other values (7)2351
15.4%

poste
Categorical

High correlation 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size95.6 KiB
Cadre Commercial
326 
Assistant de Direction
292 
Consultant
259 
Tech Lead
145 
Manager
131 
Other values (4)
317 

Length

Max length23
Median length19
Mean length15.168027
Min length7

Characters and Unicode

Total characters22297
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCadre Commercial
2nd rowAssistant de Direction
3rd rowConsultant
4th rowAssistant de Direction
5th rowConsultant

Common Values

ValueCountFrequency (%)
Cadre Commercial326
22.2%
Assistant de Direction292
19.9%
Consultant259
17.6%
Tech Lead145
9.9%
Manager131
8.9%
Senior Manager102
 
6.9%
Représentant Commercial83
 
5.6%
Directeur Technique80
 
5.4%
Ressources Humaines52
 
3.5%

Length

2026-01-16T17:03:15.961856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:16.087627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
commercial409
14.4%
cadre326
11.5%
assistant292
10.3%
de292
10.3%
direction292
10.3%
consultant259
9.1%
manager233
8.2%
tech145
 
5.1%
lead145
 
5.1%
senior102
 
3.6%
Other values (5)347
12.2%

Most occurring characters

ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)22297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

nombre_experiences_precedentes
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:16.229665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2026-01-16T17:03:16.419882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1521
35.4%
0197
 
13.4%
3159
 
10.8%
2146
 
9.9%
4139
 
9.5%
774
 
5.0%
670
 
4.8%
563
 
4.3%
952
 
3.5%
849
 
3.3%
ValueCountFrequency (%)
0197
 
13.4%
1521
35.4%
2146
 
9.9%
3159
 
10.8%
4139
 
9.5%
563
 
4.3%
670
 
4.8%
774
 
5.0%
849
 
3.3%
952
 
3.5%
ValueCountFrequency (%)
952
 
3.5%
849
 
3.3%
774
 
5.0%
670
 
4.8%
563
 
4.3%
4139
 
9.5%
3159
 
10.8%
2146
 
9.9%
1521
35.4%
0197
 
13.4%

nombre_heures_travailless
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.3 KiB
80
1470 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2940
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
801470
100.0%

Length

2026-01-16T17:03:16.721607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:16.992811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
801470
100.0%

Most occurring characters

ValueCountFrequency (%)
81470
50.0%
01470
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
81470
50.0%
01470
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
81470
50.0%
01470
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
81470
50.0%
01470
50.0%

annee_experience_totale
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:17.229525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2026-01-16T17:03:17.561937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

annees_dans_l_entreprise
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:17.878910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2026-01-16T17:03:18.210242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

annees_dans_le_poste_actuel
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:18.495701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2026-01-16T17:03:18.780505image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2372
25.3%
0244
16.6%
7222
15.1%
3135
 
9.2%
4104
 
7.1%
889
 
6.1%
967
 
4.6%
157
 
3.9%
637
 
2.5%
536
 
2.4%
Other values (9)107
 
7.3%
ValueCountFrequency (%)
0244
16.6%
157
 
3.9%
2372
25.3%
3135
 
9.2%
4104
 
7.1%
536
 
2.4%
637
 
2.5%
7222
15.1%
889
 
6.1%
967
 
4.6%
ValueCountFrequency (%)
182
 
0.1%
174
 
0.3%
167
 
0.5%
158
 
0.5%
1411
 
0.7%
1314
 
1.0%
1210
 
0.7%
1122
 
1.5%
1029
2.0%
967
4.6%

id
Real number (ℝ)

High correlation  Unique 

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.8653
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:19.096604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.02433
Coefficient of variation (CV)0.58741801
Kurtosis-1.2231789
Mean1024.8653
Median Absolute Deviation (MAD)533.5
Skewness0.01657402
Sum1506552
Variance362433.3
MonotonicityStrictly increasing
2026-01-16T17:03:19.366064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20681
 
0.1%
11
 
0.1%
21
 
0.1%
41
 
0.1%
51
 
0.1%
71
 
0.1%
81
 
0.1%
101
 
0.1%
111
 
0.1%
121
 
0.1%
Other values (1460)1460
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
41
0.1%
51
0.1%
71
0.1%
81
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
ValueCountFrequency (%)
20681
0.1%
20651
0.1%
20641
0.1%
20621
0.1%
20611
0.1%
20601
0.1%
20571
0.1%
20561
0.1%
20551
0.1%
20541
0.1%

satisfaction_employee_environnement
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Length

2026-01-16T17:03:19.507982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:19.618104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring characters

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

note_evaluation_precedente
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Length

2026-01-16T17:03:19.759881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:19.886275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

niveau_hierarchique_poste
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Length

2026-01-16T17:03:20.012331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:20.138160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

satisfaction_employee_nature_travail
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Length

2026-01-16T17:03:20.264426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:20.394499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring characters

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

satisfaction_employee_equipe
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Length

2026-01-16T17:03:20.538593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:20.658788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring characters

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

satisfaction_employee_equilibre_pro_perso
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Length

2026-01-16T17:03:20.784981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:20.911150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

eval_number
Text

Unique 

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2026-01-16T17:03:21.179408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.4564626
Min length3

Characters and Unicode

Total characters8021
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1470 ?
Unique (%)100.0%

Sample

1st rowE_1
2nd rowE_2
3rd rowE_4
4th rowE_5
5th rowE_7
ValueCountFrequency (%)
e_191
 
0.1%
e_20681
 
0.1%
e_11
 
0.1%
e_21
 
0.1%
e_41
 
0.1%
e_51
 
0.1%
e_71
 
0.1%
e_81
 
0.1%
e_101
 
0.1%
e_111
 
0.1%
Other values (1460)1460
99.3%
2026-01-16T17:03:21.972222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E1470
18.3%
_1470
18.3%
11136
14.2%
2501
 
6.2%
4468
 
5.8%
6440
 
5.5%
5437
 
5.4%
3427
 
5.3%
7423
 
5.3%
9421
 
5.2%
Other values (2)828
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)8021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E1470
18.3%
_1470
18.3%
11136
14.2%
2501
 
6.2%
4468
 
5.8%
6440
 
5.5%
5437
 
5.4%
3427
 
5.3%
7423
 
5.3%
9421
 
5.2%
Other values (2)828
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E1470
18.3%
_1470
18.3%
11136
14.2%
2501
 
6.2%
4468
 
5.8%
6440
 
5.5%
5437
 
5.4%
3427
 
5.3%
7423
 
5.3%
9421
 
5.2%
Other values (2)828
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E1470
18.3%
_1470
18.3%
11136
14.2%
2501
 
6.2%
4468
 
5.8%
6440
 
5.5%
5437
 
5.4%
3427
 
5.3%
7423
 
5.3%
9421
 
5.2%
Other values (2)828
10.3%

note_evaluation_actuelle
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Length

2026-01-16T17:03:22.304232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:22.572716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

heure_supplementaires
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size74.8 KiB
Non
1054 
Oui
416 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOui
2nd rowNon
3rd rowOui
4th rowOui
5th rowNon

Common Values

ValueCountFrequency (%)
Non1054
71.7%
Oui416
 
28.3%

Length

2026-01-16T17:03:22.857756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:23.110737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
non1054
71.7%
oui416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
N1054
23.9%
o1054
23.9%
n1054
23.9%
O416
 
9.4%
u416
 
9.4%
i416
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N1054
23.9%
o1054
23.9%
n1054
23.9%
O416
 
9.4%
u416
 
9.4%
i416
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N1054
23.9%
o1054
23.9%
n1054
23.9%
O416
 
9.4%
u416
 
9.4%
i416
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N1054
23.9%
o1054
23.9%
n1054
23.9%
O416
 
9.4%
u416
 
9.4%
i416
 
9.4%

augementation_salaire_precedente
Categorical

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size76.2 KiB
11 %
210 
13 %
209 
14 %
201 
12 %
198 
15 %
101 
Other values (10)
551 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5880
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11 %
2nd row23 %
3rd row15 %
4th row11 %
5th row12 %

Common Values

ValueCountFrequency (%)
11 %210
14.3%
13 %209
14.2%
14 %201
13.7%
12 %198
13.5%
15 %101
6.9%
18 %89
6.1%
17 %82
 
5.6%
16 %78
 
5.3%
19 %76
 
5.2%
22 %56
 
3.8%
Other values (5)170
11.6%

Length

2026-01-16T17:03:23.399228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1470
50.0%
11210
 
7.1%
13209
 
7.1%
14201
 
6.8%
12198
 
6.7%
15101
 
3.4%
1889
 
3.0%
1782
 
2.8%
1678
 
2.7%
1976
 
2.6%
Other values (6)226
 
7.7%

Most occurring characters

ValueCountFrequency (%)
11502
25.5%
1470
25.0%
%1470
25.0%
2480
 
8.2%
3237
 
4.0%
4222
 
3.8%
5119
 
2.0%
889
 
1.5%
782
 
1.4%
678
 
1.3%
Other values (2)131
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11502
25.5%
1470
25.0%
%1470
25.0%
2480
 
8.2%
3237
 
4.0%
4222
 
3.8%
5119
 
2.0%
889
 
1.5%
782
 
1.4%
678
 
1.3%
Other values (2)131
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11502
25.5%
1470
25.0%
%1470
25.0%
2480
 
8.2%
3237
 
4.0%
4222
 
3.8%
5119
 
2.0%
889
 
1.5%
782
 
1.4%
678
 
1.3%
Other values (2)131
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11502
25.5%
1470
25.0%
%1470
25.0%
2480
 
8.2%
3237
 
4.0%
4222
 
3.8%
5119
 
2.0%
889
 
1.5%
782
 
1.4%
678
 
1.3%
Other values (2)131
 
2.2%

a_quitte_l_entreprise
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size74.8 KiB
Non
1233 
Oui
237 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOui
2nd rowNon
3rd rowOui
4th rowNon
5th rowNon

Common Values

ValueCountFrequency (%)
Non1233
83.9%
Oui237
 
16.1%

Length

2026-01-16T17:03:23.726936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:23.980474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
non1233
83.9%
oui237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
N1233
28.0%
o1233
28.0%
n1233
28.0%
O237
 
5.4%
u237
 
5.4%
i237
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)4410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N1233
28.0%
o1233
28.0%
n1233
28.0%
O237
 
5.4%
u237
 
5.4%
i237
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N1233
28.0%
o1233
28.0%
n1233
28.0%
O237
 
5.4%
u237
 
5.4%
i237
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N1233
28.0%
o1233
28.0%
n1233
28.0%
O237
 
5.4%
u237
 
5.4%
i237
 
5.4%

nombre_participation_pee
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Length

2026-01-16T17:03:24.280875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:24.455015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

nb_formations_suivies
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:24.549539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2026-01-16T17:03:24.643886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
1
1470 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11470
100.0%

Length

2026-01-16T17:03:24.769898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:24.880305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
11470
100.0%

Most occurring characters

ValueCountFrequency (%)
11470
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11470
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11470
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11470
100.0%

code_sondage
Real number (ℝ)

High correlation  Unique 

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.8653
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:24.990641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.02433
Coefficient of variation (CV)0.58741801
Kurtosis-1.2231789
Mean1024.8653
Median Absolute Deviation (MAD)533.5
Skewness0.01657402
Sum1506552
Variance362433.3
MonotonicityStrictly increasing
2026-01-16T17:03:25.132808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20681
 
0.1%
11
 
0.1%
21
 
0.1%
41
 
0.1%
51
 
0.1%
71
 
0.1%
81
 
0.1%
101
 
0.1%
111
 
0.1%
121
 
0.1%
Other values (1460)1460
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
41
0.1%
51
0.1%
71
0.1%
81
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
ValueCountFrequency (%)
20681
0.1%
20651
0.1%
20641
0.1%
20621
0.1%
20611
0.1%
20601
0.1%
20571
0.1%
20561
0.1%
20551
0.1%
20541
0.1%

distance_domicile_travail
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:25.243452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2026-01-16T17:03:25.543980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

niveau_education
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Length

2026-01-16T17:03:25.670006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:25.796142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

domaine_etude
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size92.8 KiB
Infra & Cloud
606 
Transformation Digitale
464 
Marketing
159 
Entrepreunariat
132 
Autre
82 

Length

Max length23
Median length19
Mean length15.567347
Min length5

Characters and Unicode

Total characters22884
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInfra & Cloud
2nd rowInfra & Cloud
3rd rowAutre
4th rowInfra & Cloud
5th rowTransformation Digitale

Common Values

ValueCountFrequency (%)
Infra & Cloud606
41.2%
Transformation Digitale464
31.6%
Marketing159
 
10.8%
Entrepreunariat132
 
9.0%
Autre82
 
5.6%
Ressources Humaines27
 
1.8%

Length

2026-01-16T17:03:25.942636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:26.066919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
infra606
19.1%
606
19.1%
cloud606
19.1%
transformation464
14.6%
digitale464
14.6%
marketing159
 
5.0%
entrepreunariat132
 
4.2%
autre82
 
2.6%
ressources27
 
0.9%
humaines27
 
0.9%

Most occurring characters

ValueCountFrequency (%)
a2448
 
10.7%
r2198
 
9.6%
n1984
 
8.7%
i1710
 
7.5%
1703
 
7.4%
o1561
 
6.8%
t1433
 
6.3%
f1070
 
4.7%
l1070
 
4.7%
e1050
 
4.6%
Other values (18)6657
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)22884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2448
 
10.7%
r2198
 
9.6%
n1984
 
8.7%
i1710
 
7.5%
1703
 
7.4%
o1561
 
6.8%
t1433
 
6.3%
f1070
 
4.7%
l1070
 
4.7%
e1050
 
4.6%
Other values (18)6657
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2448
 
10.7%
r2198
 
9.6%
n1984
 
8.7%
i1710
 
7.5%
1703
 
7.4%
o1561
 
6.8%
t1433
 
6.3%
f1070
 
4.7%
l1070
 
4.7%
e1050
 
4.6%
Other values (18)6657
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2448
 
10.7%
r2198
 
9.6%
n1984
 
8.7%
i1710
 
7.5%
1703
 
7.4%
o1561
 
6.8%
t1433
 
6.3%
f1070
 
4.7%
l1070
 
4.7%
e1050
 
4.6%
Other values (18)6657
29.1%

ayant_enfants
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1470 
ValueCountFrequency (%)
True1470
100.0%
2026-01-16T17:03:26.183434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

frequence_deplacement
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size84.6 KiB
Occasionnel
1043 
Frequent
277 
Aucun
150 

Length

Max length11
Median length11
Mean length9.822449
Min length5

Characters and Unicode

Total characters14439
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOccasionnel
2nd rowFrequent
3rd rowOccasionnel
4th rowFrequent
5th rowOccasionnel

Common Values

ValueCountFrequency (%)
Occasionnel1043
71.0%
Frequent277
 
18.8%
Aucun150
 
10.2%

Length

2026-01-16T17:03:26.285996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-16T17:03:26.396618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
occasionnel1043
71.0%
frequent277
 
18.8%
aucun150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

annees_depuis_la_derniere_promotion
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:26.650298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2026-01-16T17:03:26.919448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
776
 
5.2%
461
 
4.1%
352
 
3.5%
545
 
3.1%
632
 
2.2%
1124
 
1.6%
818
 
1.2%
Other values (6)65
 
4.4%
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
352
 
3.5%
461
 
4.1%
545
 
3.1%
632
 
2.2%
776
 
5.2%
818
 
1.2%
917
 
1.2%
ValueCountFrequency (%)
1513
 
0.9%
149
 
0.6%
1310
 
0.7%
1210
 
0.7%
1124
 
1.6%
106
 
0.4%
917
 
1.2%
818
 
1.2%
776
5.2%
632
2.2%

annes_sous_responsable_actuel
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-16T17:03:27.196493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2026-01-16T17:03:27.477692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2344
23.4%
0263
17.9%
7216
14.7%
3142
9.7%
8107
 
7.3%
498
 
6.7%
176
 
5.2%
964
 
4.4%
531
 
2.1%
629
 
2.0%
Other values (8)100
 
6.8%
ValueCountFrequency (%)
0263
17.9%
176
 
5.2%
2344
23.4%
3142
9.7%
498
 
6.7%
531
 
2.1%
629
 
2.0%
7216
14.7%
8107
 
7.3%
964
 
4.4%
ValueCountFrequency (%)
177
 
0.5%
162
 
0.1%
155
 
0.3%
145
 
0.3%
1314
 
1.0%
1218
 
1.2%
1122
 
1.5%
1027
 
1.8%
964
4.4%
8107
7.3%

Interactions

2026-01-16T17:02:59.849958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:43.710478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:53.646354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:04.070367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:13.282109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:25.202000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.555698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:45.857265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:56.748315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:15.747912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:26.576452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:38.661738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:49.177798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:59.944767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:43.789233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:53.735131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:04.165222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:13.503664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:25.296479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.650574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:45.951859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:02.270273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:15.858017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:26.701688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:38.772095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:49.272864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:00.039572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:43.884275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:53.820724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:04.259834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:13.710300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:25.390955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.755956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:46.046911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:07.546885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:15.936761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:26.812550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:38.867437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:49.367681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:00.182021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:43.994844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:53.931515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:04.354424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:13.947354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:25.533070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.880738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:46.167848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:12.892296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:16.047408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:26.939544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:38.982640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:49.487781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:00.419305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:44.073621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:54.010685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:04.449055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:14.169256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:25.675167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.966311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:46.267355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:17.601756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:16.142297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:27.034491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:39.073520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:49.573269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:00.688369image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:44.187607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:54.120717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:04.575722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:14.407340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:25.786399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:36.076412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:46.378051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:22.914886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:16.252516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:27.145103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:39.168445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:49.684157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:00.959233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:44.288794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:54.215738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:04.686386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:14.655958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:25.896550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:36.187329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:46.488236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:28.861042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:16.363101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:27.239481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:39.278968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:49.810433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:01.224041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:44.388905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:54.326409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:04.796959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:14.865778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:26.024884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:36.313263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:46.598703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:34.772142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2026-01-16T17:02:27.349618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:39.397351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:49.920964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:10.755236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:53.157241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:03.342477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:12.474588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:24.234179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.034230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:45.320795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:56.163349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:47.191151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:26.005188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:37.838755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:48.672808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:59.329677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:11.010717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:53.253598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:03.565054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:12.585156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:24.494152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.144756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:45.431046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:56.289801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:52.654465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:26.099758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:37.949345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:48.783389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:59.439980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:11.232245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:53.331041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:03.770785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:12.680176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:24.726532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.239375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:45.525414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:56.385116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:01:58.087826image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:26.194601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:38.044421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:48.877676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:59.538438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:11.485996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:53.425478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:03.865238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:12.790827image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:24.932970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.334086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:45.636410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:56.495417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:04.442652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:26.305150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:38.393344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:48.972674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:59.629483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:03:11.764308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T16:59:53.535611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:03.967995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:13.032476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:25.090879image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:35.444625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:45.746890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:00:56.601714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:09.900120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:26.415973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:38.503833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:49.083294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-16T17:02:59.739693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2026-01-16T17:03:27.778749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
a_quitte_l_entrepriseageannee_experience_totaleannees_dans_l_entrepriseannees_dans_le_poste_actuelannees_depuis_la_derniere_promotionannes_sous_responsable_actuelaugementation_salaire_precedentecode_sondagedepartementdistance_domicile_travaildomaine_etudefrequence_deplacementgenreheure_supplementairesidid_employeenb_formations_suiviesniveau_educationniveau_hierarchique_postenombre_experiences_precedentesnombre_participation_peenote_evaluation_actuellenote_evaluation_precedenteposterevenu_mensuelsatisfaction_employee_environnementsatisfaction_employee_equilibre_pro_persosatisfaction_employee_equipesatisfaction_employee_nature_travailstatut_marital
a_quitte_l_entreprise1.0000.2130.2080.1730.1690.0270.1790.0000.0000.0770.0670.0870.1230.0090.2431.0000.0000.0790.0000.2160.1070.1980.0000.1320.2310.2170.1150.0950.0390.0990.173
age0.2131.0000.6570.2520.1980.1740.1950.010-0.0020.000-0.0190.0000.0410.0000.000-0.002-0.0020.0000.1530.2950.3530.0930.0000.0250.1750.4720.0060.0330.0350.0000.141
annee_experience_totale0.2080.6571.0000.5940.4930.3350.4950.019-0.0040.024-0.0030.0300.0000.0000.000-0.004-0.004-0.0140.0950.5390.3150.0640.0000.0000.2930.7100.0000.0000.0310.0240.069
annees_dans_l_entreprise0.1730.2520.5941.0000.8540.5200.8430.0000.0130.0000.0110.0000.0000.0660.0180.0130.0130.0010.0710.353-0.1710.0120.0000.0530.1880.4640.0310.0200.0000.0000.000
annees_dans_le_poste_actuel0.1690.1980.4930.8541.0000.5060.7250.000-0.0010.0000.0140.0000.0000.0790.042-0.001-0.0010.0050.0290.241-0.1280.0230.0310.0000.1320.3950.0360.0250.0000.0000.040
annees_depuis_la_derniere_promotion0.0270.1740.3350.5200.5061.0000.4670.0320.0080.000-0.0050.0000.0300.0000.0110.0080.0080.0100.0000.206-0.0670.0560.0000.0000.1110.2650.0000.0000.0500.0000.035
annes_sous_responsable_actuel0.1790.1950.4950.8430.7250.4671.0000.000-0.0050.0000.0040.0000.0640.0000.000-0.005-0.005-0.0120.0000.232-0.1440.0300.0300.0440.1180.3650.0000.0310.0000.0000.000
augementation_salaire_precedente0.0000.0100.0190.0000.0000.0320.0001.0000.0210.0530.0340.0000.0880.0490.0001.0000.0210.0000.0140.0000.0000.0000.9960.0530.0160.0270.0000.0000.0390.0000.000
code_sondage0.000-0.002-0.0040.013-0.0010.008-0.0050.0211.0000.0360.0390.0000.0000.0500.0161.0001.0000.0270.0450.0360.0070.0680.0290.0350.0000.0020.0000.0000.0550.0000.000
departement0.0770.0000.0240.0000.0000.0000.0000.0530.0361.0000.0000.5880.0000.0260.0001.0000.0360.0000.0000.2120.0320.0000.0000.0000.9370.1870.0180.0470.0200.0290.030
distance_domicile_travail0.067-0.019-0.0030.0110.014-0.0050.0040.0340.0390.0001.0000.0000.0230.0300.0660.0390.039-0.0250.0000.054-0.0100.0150.0580.0280.0000.0030.0000.0000.0250.0000.000
domaine_etude0.0870.0000.0300.0000.0000.0000.0000.0000.0000.5880.0001.0000.0000.0000.0001.0000.0000.0440.0550.0910.0600.0320.0000.0000.3360.0730.0310.0270.0400.0170.000
frequence_deplacement0.1230.0410.0000.0000.0000.0300.0640.0880.0000.0000.0230.0001.0000.0370.0241.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0250.0000.0000.0000.0000.035
genre0.0090.0000.0000.0660.0790.0000.0000.0490.0500.0260.0300.0000.0371.0000.0311.0000.0500.0000.0000.0480.0000.0000.0000.0000.0740.0460.0000.0000.0000.0000.032
heure_supplementaires0.2430.0000.0000.0180.0420.0110.0000.0000.0160.0000.0660.0000.0240.0311.0001.0000.0160.0990.0010.0000.0000.0000.0000.0000.0000.0000.0600.0000.0250.0220.000
id1.000-0.002-0.0040.013-0.0010.008-0.0051.0001.0001.0000.0391.0001.0001.0001.0001.0001.0000.0271.0001.0000.0071.0001.0001.0001.0000.0021.0001.0001.0001.0001.000
id_employee0.000-0.002-0.0040.013-0.0010.008-0.0050.0211.0000.0360.0390.0000.0000.0500.0161.0001.0000.0270.0450.0360.0070.0680.0290.0350.0000.0020.0000.0000.0550.0000.000
nb_formations_suivies0.0790.000-0.0140.0010.0050.010-0.0120.0000.0270.000-0.0250.0440.0000.0000.0990.0270.0271.0000.0270.017-0.0470.0000.0000.0130.000-0.0350.0000.0000.0000.0210.000
niveau_education0.0000.1530.0950.0710.0290.0000.0000.0140.0450.0000.0000.0550.0000.0000.0011.0000.0450.0271.0000.0880.1010.0270.0000.0000.0510.0940.0190.0000.0160.0150.000
niveau_hierarchique_poste0.2160.2950.5390.3530.2410.2060.2320.0000.0360.2120.0540.0910.0000.0480.0001.0000.0360.0170.0881.0000.1130.0690.0000.0000.5690.8640.0000.0000.0000.0000.046
nombre_experiences_precedentes0.1070.3530.315-0.171-0.128-0.067-0.1440.0000.0070.032-0.0100.0600.0000.0000.0000.0070.007-0.0470.1010.1131.0000.0000.0000.0000.0790.1900.0000.0510.0000.0000.038
nombre_participation_pee0.1980.0930.0640.0120.0230.0560.0300.0000.0680.0000.0150.0320.0000.0000.0001.0000.0680.0000.0270.0690.0001.0000.0000.0220.0390.0560.0000.0190.0300.0000.581
note_evaluation_actuelle0.0000.0000.0000.0000.0310.0000.0300.9960.0290.0000.0580.0000.0000.0000.0001.0000.0290.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0260.000
note_evaluation_precedente0.1320.0250.0000.0530.0000.0000.0440.0530.0350.0000.0280.0000.0160.0000.0001.0000.0350.0130.0000.0000.0000.0220.0001.0000.0000.0460.0340.0000.0000.0000.024
poste0.2310.1750.2930.1880.1320.1110.1180.0160.0000.9370.0000.3360.0000.0740.0001.0000.0000.0000.0510.5690.0790.0390.0000.0001.0000.4230.0000.0290.0300.0000.061
revenu_mensuel0.2170.4720.7100.4640.3950.2650.3650.0270.0020.1870.0030.0730.0250.0460.0000.0020.002-0.0350.0940.8640.1900.0560.0000.0460.4231.0000.0000.0000.0430.0000.061
satisfaction_employee_environnement0.1150.0060.0000.0310.0360.0000.0000.0000.0000.0180.0000.0310.0000.0000.0601.0000.0000.0000.0190.0000.0000.0000.0000.0340.0000.0001.0000.0000.0000.0000.019
satisfaction_employee_equilibre_pro_perso0.0950.0330.0000.0200.0250.0000.0310.0000.0000.0470.0000.0270.0000.0000.0001.0000.0000.0000.0000.0000.0510.0190.0000.0000.0290.0000.0001.0000.0000.0000.000
satisfaction_employee_equipe0.0390.0350.0310.0000.0000.0500.0000.0390.0550.0200.0250.0400.0000.0000.0251.0000.0550.0000.0160.0000.0000.0300.0000.0000.0300.0430.0000.0001.0000.0000.025
satisfaction_employee_nature_travail0.0990.0000.0240.0000.0000.0000.0000.0000.0000.0290.0000.0170.0000.0000.0221.0000.0000.0210.0150.0000.0000.0000.0260.0000.0000.0000.0000.0000.0001.0000.000
statut_marital0.1730.1410.0690.0000.0400.0350.0000.0000.0000.0300.0000.0000.0350.0320.0001.0000.0000.0000.0000.0460.0380.5810.0000.0240.0610.0610.0190.0000.0250.0001.000

Missing values

2026-01-16T17:03:12.214210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-16T17:03:13.433934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_employeeagegenrerevenu_mensuelstatut_maritaldepartementpostenombre_experiences_precedentesnombre_heures_travaillessannee_experience_totaleannees_dans_l_entrepriseannees_dans_le_poste_actuelidsatisfaction_employee_environnementnote_evaluation_precedenteniveau_hierarchique_postesatisfaction_employee_nature_travailsatisfaction_employee_equipesatisfaction_employee_equilibre_pro_persoeval_numbernote_evaluation_actuelleheure_supplementairesaugementation_salaire_precedentea_quitte_l_entreprisenombre_participation_peenb_formations_suiviesnombre_employee_sous_responsabilitecode_sondagedistance_domicile_travailniveau_educationdomaine_etudeayant_enfantsfrequence_deplacementannees_depuis_la_derniere_promotionannes_sous_responsable_actuel
0141F5993CélibataireCommercialCadre Commercial8808641232411E_13Oui11 %Oui001112Infra & CloudYOccasionnel05
1249M5130Marié(e)ConsultingAssistant de Direction180101072322243E_24Non23 %Non131281Infra & CloudYFrequent17
2437M2090CélibataireConsultingConsultant6807004421323E_43Oui15 %Oui031422AutreYOccasionnel00
3533F2909Marié(e)ConsultingAssistant de Direction1808875431333E_53Oui11 %Non031534Infra & CloudYFrequent30
4727M3468Marié(e)ConsultingConsultant9806227131243E_73Non12 %Non131721Transformation DigitaleYOccasionnel22
5832M3068CélibataireConsultingConsultant0808778431432E_83Non13 %Non021822Infra & CloudYFrequent36
61059F2670Marié(e)ConsultingConsultant480121010341112E_104Oui20 %Non3311033Transformation DigitaleYOccasionnel00
71130M2693Divorcé(e)ConsultingConsultant18011011431323E_114Non22 %Non12111241Infra & CloudYOccasionnel00
81238M9526CélibataireConsultingTech Lead080109712423323E_124Non21 %Non02112233Infra & CloudYFrequent18
91336M5237Marié(e)ConsultingManager680177713332322E_133Non13 %Non23113273Transformation DigitaleYOccasionnel77
id_employeeagegenrerevenu_mensuelstatut_maritaldepartementpostenombre_experiences_precedentesnombre_heures_travaillessannee_experience_totaleannees_dans_l_entrepriseannees_dans_le_poste_actuelidsatisfaction_employee_environnementnote_evaluation_precedenteniveau_hierarchique_postesatisfaction_employee_nature_travailsatisfaction_employee_equipesatisfaction_employee_equilibre_pro_persoeval_numbernote_evaluation_actuelleheure_supplementairesaugementation_salaire_precedentea_quitte_l_entreprisenombre_participation_peenb_formations_suiviesnombre_employee_sous_responsabilitecode_sondagedistance_domicile_travailniveau_educationdomaine_etudeayant_enfantsfrequence_deplacementannees_depuis_la_derniere_promotionannes_sous_responsable_actuel
1460205429F3785CélibataireConsultingAssistant de Direction1805542054421121E_20543Non14 %Non0312054284Transformation DigitaleYOccasionnel04
1461205550M10854Divorcé(e)CommercialCadre Commercial48020322055423123E_20553Oui13 %Oui1312055283MarketingYOccasionnel20
1462205639F12031Marié(e)CommercialCadre Commercial080212092056224412E_20563Non11 %Non1212056241MarketingYOccasionnel96
1463205731M9936CélibataireConsultingTech Lead08010942057232123E_20573Non19 %Non021205753Transformation DigitaleYAucun17
1464206026F2966CélibataireCommercialReprésentant Commercial0805422060421343E_20603Non18 %Non021206053AutreYOccasionnel00
1465206136M2571Marié(e)ConsultingConsultant48017522061342433E_20613Non17 %Non1312061232Transformation DigitaleYFrequent03
1466206239M9991Marié(e)ConsultingManager4809772062423113E_20623Non15 %Non151206261Transformation DigitaleYOccasionnel17
1467206427M6142Marié(e)ConsultingTech Lead1806622064242223E_20644Oui20 %Non101206443Infra & CloudYOccasionnel03
1468206549M5390Marié(e)CommercialCadre Commercial28017962065422242E_20653Non14 %Non031206523Transformation DigitaleYFrequent08
1469206834M4404Marié(e)ConsultingConsultant2806432068242314E_20683Non12 %Non031206883Transformation DigitaleYOccasionnel12